WeChat Mini Program
Old Version Features

A Highly Sensitive Light-Induced Thermoelastic Spectroscopy Sensor Using a Charge Amplifier to Improve the Signal-to-Noise Ratio

Sensors (Basel, Switzerland)(2025)

Cited 0|Views1
Abstract
A highly sensitive light-induced thermoelastic spectroscopy (LITES) sensor employing a charge amplifier (CA) is reported for the first time in this invited paper. CA has the merits of high input impedance and strong anti-interference ability. The usually used transimpedance amplifier (TA) and voltage amplifier (VA) were also studied under the same conditions for comparison. A standard commercial quartz tuning fork (QTF) with a resonant frequency of approximately 32.76 kHz was used as the photothermal signal transducer. Methane (CH4) was used as the target gas in these sensors for performance verification. Compared to the TA-LITES sensor and VA-LITES sensor, the reported CA-LITES sensor shows improvements of 1.83 times and 5.28 times in the minimum detection limit (MDL), respectively. When compared to the LITES sensor without an amplifier (WA-LITES), the MDL has a 19.96-fold improvement. After further optimizing the gain of the CA, the MDL of the CA-LITES sensor was calculated as 2.42 ppm, which further improved the performance of the MDL by 30.3 times compared to the WA-LITES. Additionally, long-term stability is analyzed using Allan deviation analysis. When the average time of the sensor system is increased to 50 s, the MDL of the CA-LITES sensor system can be improved to 0.58 ppm.
More
Translated text
Key words
methane (CH,) detection,light-induced thermoelastic spectroscopy (LITES),quartz tuning fork (QTF),charge amplifier,minimum detection limit (MDL)
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文首次报道了一种采用电荷放大器的光诱导热弹性光谱传感器,大幅提高了信噪比,实现了对目标气体甲烷的高灵敏度检测。

方法】:使用电荷放大器(CA)替代传统的传输阻抗放大器(TA)和电压放大器(VA),利用标准商用石英音叉作为光热信号传感器。

实验】:通过对比实验,在相同条件下,CA-LITES传感器在最小检测限(MDL)上分别比TA-LITES传感器和VA-LITES传感器提高了1.83倍和5.28倍,比无放大器的LITES传感器(WA-LITES)提高了19.96倍;进一步优化电荷放大器的增益后,MDL达到2.42 ppm,比WA-LITES提高了30.3倍。使用Allan偏差分析研究了长期稳定性,当平均时间增加到50秒时,MDL可改善至0.58 ppm。实验使用的数据集为甲烷(CH4)气体。